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A literature review on quantitative models for supply chain risk management: Can they be applied to pandemic disruptions?

Authors :
Rinaldi M
Murino T
Gebennini E
Morea D
Bottani E
Source :
Computers & industrial engineering [Comput Ind Eng] 2022 Aug; Vol. 170, pp. 108329. Date of Electronic Publication: 2022 Jun 15.
Publication Year :
2022

Abstract

Supply chain risk management is considered a topic of increasing interest worldwide and its focus has evolved over time. The recent coronavirus pandemic (known as COVID-19) has forced business to handle a new global crisis and rapidly adapt to unexpected challenges. In an attempt to help companies counteract the pandemic risk, as well as to fuel the scientific discussion about this topic, this paper proposes a systematic literature review on risk management and disruptions in the supply chain focusing on quantitative models and paying a particular attention to highlighting the potentials of the studies reviewed for being applied to counteract pandemic emergencies. An appropriate query was made on Scopus and returned, after a manual screening, a useful set of 99 papers that proposed models for supply chain risk management. The relevant aspects of pandemics risk management have been first identified and mapped; then, the studies reviewed have been analysed with the aim of evaluating their suitability of being applied to sanitary crises. In carrying out this review of the literature, the study moves from previous, more general, reviews about risk management and updates them, starting from the lines of research that have been covered in recent years and evaluating their consistency with future research directions emerging also as a consequence of the pandemic crisis. Gaps and limitations of the existing models are identified and future research directions for pandemics risk management are suggested.<br />Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (© 2022 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1879-0550
Volume :
170
Database :
MEDLINE
Journal :
Computers & industrial engineering
Publication Type :
Academic Journal
Accession number :
35722204
Full Text :
https://doi.org/10.1016/j.cie.2022.108329